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Register a new userFully Hyperbolic Graph Convolution Network for Recommendation
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Added by
Liping Wang
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Recently, Graph Convolution Network (GCN) based methods have achieved outstanding performance for recommendation. These methods embed users and items in Euclidean space, and perform graph convolution on user-item interaction graphs. However, real-world datasets usually exhibit tree-like hierarchical structures, which make Euclidean space less effective in capturing user-item relationship. In contrast, hyperbolic space, as a continuous analogue of a tree-graph, provides a promising alternative. In this paper, we propose a fully hyperbolic GCN model for recommendation, where all operations are performed in hyperbolic space. Utilizing the advantage of hyperbolic space, our method is able to embed users/items with less distortion and capture user-item interaction relationship more accurately. Extensive experiments on public benchmark datasets show that our method outperforms both Euclidean and hyperbolic counterparts and requires far lower embedding dimensionality to achieve comparable performance.
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Graph convolutional networks (GCNs) have recently enabled a popular class of algorithms for collaborative filtering (CF). Nevertheless, the theoretical underpinnings of their empirical successes remain elusive. In this paper, we endeavor to obtain a better understanding of GCN-based CF methods via the lens of graph signal processing. By identifying the critical role of smoothness, a key concept in graph signal processing, we develop a unified graph convolution-based framework for CF. We prove that many existing CF methods are special cases of this framework, including the neighborhood-based methods, low-rank matrix factorization, linear auto-encoders, and LightGCN, corresponding to different low-pass filters. Based on our framework, we then present a simple and computationally efficient CF baseline, which we shall refer to as Graph Filter based Collaborative Filtering (GF-CF). Given an implicit feedback matrix, GF-CF can be obtained in a closed form instead of expensive training with back-propagation. Experiments will show that GF-CF achieves competitive or better performance against deep learning-based methods on three well-known datasets, notably with a $70%$ performance gain over LightGCN on the Amazon-book dataset.
Session-based recommendation (SBR) learns users preferences by capturing the short-term and sequential patterns from the evolution of user behaviors. Among the studies in the SBR field, graph-based approaches are a relatively powerful kind of way, which generally extract item information by message aggregation under Euclidean space. However, such methods cant effectively extract the hierarchical information contained among consecutive items in a session, which is critical to represent users preferences. In this paper, we present a hyperbolic contrastive graph recommender (HCGR), a principled session-based recommendation framework involving Lorentz hyperbolic space to adequately capture the coherence and hierarchical representations of the items. Within this framework, we design a novel adaptive hyperbolic attention computation to aggregate the graph message of each users preference in a session-based behavior sequence. In addition, contrastive learning is leveraged to optimize the item representation by considering the geodesic distance between positive and negative samples in hyperbolic space. Extensive experiments on four real-world datasets demonstrate that HCGR consistently outperforms state-of-the-art baselines by 0.43$%$-28.84$%$ in terms of $HitRate$, $NDCG$ and $MRR$.
In recent years, graph neural networks (GNNs) have shown powerful ability in collaborative filtering, which is a widely adopted recommendation scenario. While without any side information, existing graph neural network based methods generally learn a one-hot embedding for each user or item as the initial input representation of GNNs. However, such one-hot embedding is intrinsically transductive, making these methods with no inductive ability, i.e., failing to deal with new users or new items that are unseen during training. Besides, the number of model parameters depends on the number of users and items, which is expensive and not scalable. In this paper, we give a formal definition of inductive recommendation and solve the above problems by proposing Inductive representation based Graph Convolutional Network (IGCN) for collaborative filtering. Specifically, we design an inductive representation layer, which utilizes the interaction behavior with core users or items as the initial representation, improving the general recommendation performance while bringing inductive ability. Note that, the number of parameters of IGCN only depends on the number of core users or items, which is adjustable and scalable. Extensive experiments on three public benchmarks demonstrate the state-of-the-art performance of IGCN in both transductive and inductive recommendation scenarios, while with remarkably fewer model parameters. Our implementations are available here in PyTorch.
Using reviews to learn user and item representations is important for recommender system. Current review based methods can be divided into two categories: (1) the Convolution Neural Network (CNN) based models that extract n-gram features from user/item reviews; (2) the Recurrent Neural Network (RNN) based models that learn global contextual representations from reviews for users and items. Despite their success, both CNN and RNN based models in previous studies suffer from their own drawbacks. While CNN based models are weak in modeling long-dependency relation in text, RNN based models are slow in training and inference due to their incapability with parallel computing. To alleviate these problems, we propose a new text encoder module for review modeling in recommendation by combining convolution networks with self-attention networks to model local and global interactions in text together.As different words, sentences, reviews have different importance for modeling user and item representations, we construct review models hierarchically in sentence-level, review-level, and user/item level by encoding words for sentences, encoding sentences for reviews, and encoding reviews for user and item representations. Experiments on Amazon Product Benchmark show that our model can achieve significant better performance comparing to the state-of-the-art review based recommendation models.
Heterogeneous information network (HIN) is widely applied to recommendation systems due to its capability of modeling various auxiliary information with meta-path. However, existing HIN-based recommendation models usually fuse the information from various meta-paths by simple weighted sum or concatenation, which limits the improvement of performance because it lacks the capability of interest compositions among meta-paths. In this article, we propose a HIN-based Interest Composition model for Recommendation (HicRec). Specially, the representations of users and items are learnt with graph neural network on both graph structure and features in each meta-path, and a parameter sharing mechanism is utilized here to ensure the representations of users and items are in the same latent space. Then, users interest on each item from each pair of related meta-paths is calculated by a combination of the representations of users and items. The composed interests of users are obtained by a composition of them from both intra- and inter-meta-paths for recommendation. Extensive experiments are conducted on three real-world datasets and the results demonstrate the outperformance of our proposed HicRec against the baselines.
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